63,630 research outputs found
Computer-Aided System for Wind Turbine Data Analysis
Context: The current work on wind turbine failure detection focuses on researching suitable signal processing algorithms and developing efficient diagnosis algorithms. The laboratory research would involve large and complex data, and it can be a daunting task.
Aims: To develop a Computer-Aided system for assisting experts to conduct an efficient laboratory research on wind turbine data analysis. System is expected to provide data visualization, data manipulation, massive data processing and wind turbine failure detection.
Method: 50G off-line SCADA data and 4 confident diagnosis algorithms were used in this project. Apart from the instructions from supervisor, this project also gained help from two experts from Engineering Department. Java and Microsoft SQL database were used to develop the system.
Results: Data visualization provided 6 different charting solutions and together with robust user interactions. 4 failure diagnosis solutions and data manipulations were provided in the system. In addition, dedicated database server and Matlab API with Java RMI were used to resolve the massive data processing problem.
Conclusions: Almost all of the deliverables were completed. Friendly GUI and useful functionalities make user feel more comfortable. The final product does enable experts to conduct an efficient laboratory research. The end of this project also gave some potential extensions of the system
Driving continuous improvement
The quality of improvement depends on the quality of leading and lagging performance indicators. For this reason, several tools, such as process mapping, cause and effect analysis and FMEA, need to be used in an integrated way with performance measurement models, such as balanced scorecard, integrated performance measurement system, performance prism and so on. However, in our experience, this alone is not quite enough due to the amount of effort required to monitor performance indicators at operational levels. The authors find that IT support is key to the successful implementation of performance measurement-driven continuous improvement schemes
Automated Dynamic Firmware Analysis at Scale: A Case Study on Embedded Web Interfaces
Embedded devices are becoming more widespread, interconnected, and
web-enabled than ever. However, recent studies showed that these devices are
far from being secure. Moreover, many embedded systems rely on web interfaces
for user interaction or administration. Unfortunately, web security is known to
be difficult, and therefore the web interfaces of embedded systems represent a
considerable attack surface.
In this paper, we present the first fully automated framework that applies
dynamic firmware analysis techniques to achieve, in a scalable manner,
automated vulnerability discovery within embedded firmware images. We apply our
framework to study the security of embedded web interfaces running in
Commercial Off-The-Shelf (COTS) embedded devices, such as routers, DSL/cable
modems, VoIP phones, IP/CCTV cameras. We introduce a methodology and implement
a scalable framework for discovery of vulnerabilities in embedded web
interfaces regardless of the vendor, device, or architecture. To achieve this
goal, our framework performs full system emulation to achieve the execution of
firmware images in a software-only environment, i.e., without involving any
physical embedded devices. Then, we analyze the web interfaces within the
firmware using both static and dynamic tools. We also present some interesting
case-studies, and discuss the main challenges associated with the dynamic
analysis of firmware images and their web interfaces and network services. The
observations we make in this paper shed light on an important aspect of
embedded devices which was not previously studied at a large scale.
We validate our framework by testing it on 1925 firmware images from 54
different vendors. We discover important vulnerabilities in 185 firmware
images, affecting nearly a quarter of vendors in our dataset. These
experimental results demonstrate the effectiveness of our approach
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Uncertainty explicit assessment of off-the-shelf software: Selection of an optimal diverse pair
Assessment of software COTS components is an essential part of component-based software development. Sub-optimal selection of components may lead to solutions with low quality. The assessment is based on incomplete knowledge about the COTS components themselves and other aspects, which may affect the choice such as the vendor's credentials, etc. We argue in favor of assessment methods in which uncertainty is explicitly represented (`uncertainty explicit' methods) using probability distributions. We have adapted a model (developed elsewhere by Littlewood, B. et al. (2000)) for assessment of a pair of COTS components to take account of the fault (bug) logs that might be available for the COTS components being assessed. We also provide empirical data from a study we have conducted with off-the-shelf database servers, which illustrate the use of the method
Software Aging Analysis of Web Server Using Neural Networks
Software aging is a phenomenon that refers to progressive performance
degradation or transient failures or even crashes in long running software
systems such as web servers. It mainly occurs due to the deterioration of
operating system resource, fragmentation and numerical error accumulation. A
primitive method to fight against software aging is software rejuvenation.
Software rejuvenation is a proactive fault management technique aimed at
cleaning up the system internal state to prevent the occurrence of more severe
crash failures in the future. It involves occasionally stopping the running
software, cleaning its internal state and restarting it. An optimized schedule
for performing the software rejuvenation has to be derived in advance because a
long running application could not be put down now and then as it may lead to
waste of cost. This paper proposes a method to derive an accurate and optimized
schedule for rejuvenation of a web server (Apache) by using Radial Basis
Function (RBF) based Feed Forward Neural Network, a variant of Artificial
Neural Networks (ANN). Aging indicators are obtained through experimental setup
involving Apache web server and clients, which acts as input to the neural
network model. This method is better than existing ones because usage of RBF
leads to better accuracy and speed in convergence.Comment: 11 pages, 8 figures, 1 table; International Journal of Artificial
Intelligence & Applications (IJAIA), Vol.3, No.3, May 201
Cloud WorkBench - Infrastructure-as-Code Based Cloud Benchmarking
To optimally deploy their applications, users of Infrastructure-as-a-Service
clouds are required to evaluate the costs and performance of different
combinations of cloud configurations to find out which combination provides the
best service level for their specific application. Unfortunately, benchmarking
cloud services is cumbersome and error-prone. In this paper, we propose an
architecture and concrete implementation of a cloud benchmarking Web service,
which fosters the definition of reusable and representative benchmarks. In
distinction to existing work, our system is based on the notion of
Infrastructure-as-Code, which is a state of the art concept to define IT
infrastructure in a reproducible, well-defined, and testable way. We
demonstrate our system based on an illustrative case study, in which we measure
and compare the disk IO speeds of different instance and storage types in
Amazon EC2
Analysis of Software Aging in a Web Server
A number of recent studies have reported the phenomenon of âsoftware agingâ, characterized by progressive performance degradation and/or an increased occurrence rate of hang/crash failures of a software system due to the exhaustion of operating system resources or the accumulation of errors. To counteract this phenomenon, a proactive technique called 'software rejuvenation' has been proposed. It essentially involves stopping the running software, cleaning its internal state and/or its environment and then restarting it. Software rejuvenation, being preventive in nature, begs the question as to when to schedule it. Periodic rejuvenation, while straightforward to implement, may not yield the best results, because the rate at which software ages is not constant, but it depends on the time-varying system workload. Software rejuvenation should therefore be planned and initiated in the face of the actual system behavior. This requires the measurement, analysis and prediction of system resource usage. In this paper, we study the development of resource usage in a web server while subjecting it to an artificial workload. We first collect data on several system resource usage and activity parameters. Non-parametric statistical methods are then applied for detecting and estimating trends in the data sets. Finally, we fit time series models to the data collected. Unlike the models used previously in the research on software aging, these time series models allow for seasonal patterns, and we show how the exploitation of the seasonal variation can help in adequately predicting the future resource usage. Based on the models employed here, proactive management techniques like software rejuvenation triggered by actual measurements can be built. --Software aging,software rejuvenation,Linux,Apache,web server,performance monitoring,prediction of resource utilization,non-parametric trend analysis,time series analysis
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